Supplement to Statistics Canada's Generic Privacy Impact Assessment related to the Canadian Correctional Services Survey (CCSS)

Date: March 2023

Program manager: Director, Canadian Centre for Justice and Community Safety Statistics
Director General, Health, Justice, Diversity, and Population

Reference to Personal Information Bank (PIB)

The Canadian Correctional Services Survey was originally considered covered by the Justice Research bank (StatCan PPU 028), however given the expanded nature of the survey and sensitivity of the personal information being collected, a new bank is being requested.

In accordance with the Privacy Act, Statistics Canada is submitting a new institutional personal information bank (PIB) to describe any personal information obtained from the Canadian Correctional Services Survey, for the purposes of the Statistics Act. The following PIB is proposed for review and registration.

Canadian Correctional Services Survey (CCSS)

Description: This bank describes information that is obtained from federal and provincial/territorial correctional services programs in Canada on adults and youth being supervised by correctional services. Personal information may include name, date of birth, sex, Indigenous identity, visible minority group, municipality, postal code, social insurance number, fingerprint section identification number, provincial/territorial health insurance number and provincial/territorial driver's license number.

Class of Individuals: Adults and youth being supervised by provincial/territorial or federal correctional services programs in Canada.

Purpose: The personal information is used to produce statistical data and analyses at a disaggregated level on the federal, provincial/territorial populations supervised under correctional services in Canada. Personal information is collected pursuant to the Statistics Act (Sections 3, 7, 13, 22 (d)).

Consistent Uses: Subject to Statistics Canada's Directive on Microdata Linkage, information on adults and youth being supervised by correctional services may be combined with the Census of Population and the National Household Survey for disaggregated data evaluation, with data on the military veteran population, as well as with key health datasets to better understand the prevalence of mental health issues in the correctional population. Furthermore, CCSS data will be used to produce counts of residents in correctional facilities for the Census of Population collective dwelling counts.

Retention and Disposal Standards: Information is retained until it is no longer required for statistical purposes and then it is destroyed.

RDA Number: 2018/001

Related Record Number: StatCan CCJ 135

TBS Registration: To be assigned by TBS

Bank Number: StatCan PPU 023

Description of statistical activity

Under the authority of the Statistics ActFootnote 1, Statistics Canada's Canadian Centre for Justice and Community Safety Statistics (CCJCSS) conducts the Canadian Correctional Services Survey (CCSS), an administrative dataFootnote 2 survey that collects microdata on adults and youth electronically from correctional services programs in Canada. The objective of the survey is to be a source of national information on corrections, which is directly related to the mandate of the CCJCSS of providing information to the justice community and the public on the nature and extent of crime and victimization and the administration of criminal and civil justice in Canada.

The CCJCSS is the focal point of a federal-provincial-territorial partnership for the collection of justice information in Canada. This partnership, known as the National Justice Statistics Initiative (NJSI), is composed of representatives of the federal, provincial and territorial governments responsible for the administration of justice in Canada, and Statistics Canada. Development of the CCSS was guided by the NJSI to fill data needs and inform federal and provincial policy makers in the field of justice and public safety, managers of correctional services programs, researchers, academics and the public, on key indicators related to the correctional population.

One of the most important needs is information related to repeated involvement with the criminal justice system, a key justice priority identified by Deputy Ministers responsible for Justice and Public Safety, as well as other policy makers and justice administrators. To respond to this need, Public Safety Canada and the CCJCSS developed an ongoing pan-Canadian program of repeated contact – or "re-contact" – with the criminal justice system. The CCSS contributes the correctional services information needed for this program. In addition, recent consultation through the Engagement on Corrections Representation Data and Analysis Strategy involved respondents from a wide and diverse range of perspectives, including: Indigenous and racialized groups and organizations; corrections agencies; academics; and other interested parties at the national and provincial/teritorial government levels. The Engagement identified the need for Statistics Canada to develop population-based indicators and re-contact indicators using disaggregated data to measure representation of sub-populations in correctional systems, as well as the need to further analyze relationships between socio-economic and mental health issues and over-representation. The CCSS is the only high-quality source of information on individuals under supervision within the correctional system that can be combined with information on the general population to provide these indicators and allow analysis of these critical justice issues to meet data needs.

Statistics Canada began development of the CCSS in 2014 and began collection in 2016. The survey is currently implemented in six jurisdictions: Newfoundland and Labrador (youth corrections only), Nova Scotia, Ontario (adult corrections only), Saskatchewan, Alberta and British Columbia. The CCJCSS is now expanding the coverage of the survey to include the remaining provincial and territorial correctional services, as well as federal correctional services. These stakeholders are:

  • Newfoundland and Labrador Justice and Public Safety
  • Prince Edward Island Community and Correctional Services
  • New-Brunswick Public Safety
  • Ministère de la sécurité publique du Québec
  • Ministère de la santé et des services sociaux du Québec
  • Ontario Children Community and Social Services- youth division
  • Manitoba Justice - Corrections
  • Yukon Correctional Services
  • Yukon Health and Social Services
  • Northwest Territories Department of Justice, Corrections Service
  • Nunavut Justice - Corrections
  • Correctional Service Canada

To achieve the survey's objective, Statistics Canada collects personal information on individuals under correctional supervision across the country including:

  • direct identifiers of persons supervised by corrections (where available and agreed to by the correctional program):
    • name
    • aliases
    • address (postal code)
    • date of birth
    • Social Insurance Number
    • FPS-CPIC number
    • Driver's Licence Number
    • Health Insurance Number
  • demographic information of persons being supervised (e.g., sex, Indigenous identity, racialized group)
  • their legal hold status while in correctional services
  • offences and conditions related to various court orders
  • events related to the person that occur during the period of supervision
  • results of any needs assessments done on persons while in correctional services.

The CCSS provides information to the public, media, academics and researchers on trends in correctional services, as well as demographic information on the population under correctional supervision in Canada. Survey results, including information on admissions to correctional services as well as the number of persons supervised by correctional services and their characteristics, are published annually in a series of data tables on the Statistics Canada website. In addition, special topic analyses in JuristatFootnote 3 publications as well as record linkage studies using CCSS data explore key issues facing the criminal justice system. Expanding coverage of the CCSS means that these measures and analyses can be produced at the national level, meeting the data needs and gaps currently identified by provincial/territorial and federal justice stakeholders.

To-date, linkage of the CCSS to internal statistical databases, more specifically linkage of the CCSS to the Census of Population and the National Household Survey for disaggregated data evaluation, as well as linkage to the Uniform Crime Reporting (UCR2) Survey and the Integrated Criminal Court Survey (ICCS) to study re-contact with the criminal justice system, have been undertaken. Statistics Canada's microdata linkage and related statistical activities were assessed in Statistics Canada's Generic Privacy Impact Assessment.Footnote 4 All data linkage activities are subject to established governanceFootnote 5, and are assessed against the privacy principles as well as necessity and proportionalityFootnote 6. All approved linkages are published on Statistics Canada's websiteFootnote 7.

Analytical files will be used by Statistics Canada to produce non-confidential aggregate statistical tables and analytical reports, such as reports for Juristat. Anonymized CCSS analytical files, as well as integrated corrections and criminal court data will also be placed in Statistics Canada's Research Data Centres (RDCs)Footnote 8 to facilitate research on key justice issues such as re-contact, within a secure research environment. Confidentiality vetting guidelines specific to the CCSS will be developed to prevent the release of potentially sensitive information that pertains to the characteristics of a particular individual. Researchers must become deemed employees of Statistics Canada to access the files in the RDCs. Additionally, access will only be granted once a research proposal has been approved.

Future plans under consideration also include linkage with data on the military veteran population, as well as linkage with other datasets via the Social Data Linkage Environment (SDLE)Footnote 9 to explore issues relevant to the justice community (for example, the prevalence of mental health issues in the correctional population). Furthermore, CCSS data will be used to produce counts of residents in correctional facilities for the Census of Population collective dwelling countsFootnote 10.

Reason for supplement

While the Generic Privacy Impact Assessment (PIA) addresses most of the privacy and security risks related to statistical activities conducted by Statistics Canada, this supplement was developed due to the breadth (both in terms of the number of variables being collected as well as the expanded jurisdictional coverage) and overall sensitivity of the personal information being requested with relation to the affected individuals. Further, personal information collection includes youth, which further raises the sensitivity level of the collection of personal information. As is the case with all PIAs, Statistics Canada's privacy framework ensures that elements of privacy protection and privacy controls are documented and applied.

Necessity and Proportionality

The use of personal information for the activity can be justified against Statistics Canada's Necessity and Proportionality Framework:

  1. Necessity: Information from the CCSS informs correctional services programs on the need for and development of programming to address specific needs (i.e., physical and mental health of persons under correctional supervision, rehabilitation and treatment programs,) as well as manage facility capacity and case flow – resulting in many potential and direct benefits to the covered populations.

    Statistics Canada requires the personal information to produce accurate information on the correctional population in Canada to fulfill the agency's statistical mandate, and specifically to produce valuable demographic information at a disaggregated level on the federal, provincial and territorial populations supervised under correctional services. The CCSS national data requirements (i.e., survey variables) were developed in consultation with the National Justice Statistics Initiative (NJSI), the federal-provincial-territorial partnership for the collection of information on the nature and extent of crime and the administration of civil and criminal justice in Canada.

    In 2021, Statistics Canada also engaged numerous partners of interest, including Indigenous and racialized community groups and organizations, and sought input through the Engagement on Corrections Representation Data and Analysis Strategy, on the development of its statistical program, including the CCSS. The engagement identified several data needs, notably including the need for Statistics Canada to develop population-based indicators and re-contact indicators using disaggregated data to measure representation of sub-populations in correctional systems, as well as the need to further analyze relationships between socio-economic and mental health issues and over-representation.

    The CCSS allows for the development of these indicators, as well as record linkage opportunities to meet these research needs. For example, information on offender characteristics collected by the CCSS, such as sex, Indigenous identity and racialized group, allows the integration of corrections and population statistics to create population-based metrics needed to address issues such as the overrepresentation of certain groups (e.g., Indigenous peoples and the Black population) within correctional services programs across the country.

    Furthermore, concerns related to the overrepresentation of Indigenous and racialized individuals in the Canadian criminal justice system reveal important gaps in the availability of disaggregated dataFootnote 11. Full CCSS coverage allows the disaggregation of data and the ability to study socio-economic factors impacting overrepresentation, such as mental health, substance use, homelessness, income, and education, not only within correctional services, but within the broader social and justice systems. CCSS data can help inform correctional services programs on the need for and development of programming to address specific needs of those involved under correctional supervision, for example mental health needs, rehabilitation and treatment programming, as well as programs aimed at successful community integration.

    The personal identifiers collected by the CCSS enable record linkage of CCSS data with key health administrative datasets (such as Vital Statistics, National Ambulatory Care Reporting System and the Discharge Abstract Database) to better understand, for example, re-contact, overrepresentation, and the prevalence of mental health issues in the correctional population. This information is needed to meet the data gaps and needs identified by Deputy Ministers responsible for Justice and Public Safety, as well as other policy makers and justice stakeholders including all provincial, territorial and federal correctional services programs in Canada. Insight from the CCSS provides the social and economic context of the correctional population and allows evidence-based decision making. The full national picture of the correctional system, needed for comprehensive re-contact analysis for example, is only possible with the participation of all jurisdictions in the CCSS. Full coverage allows for analysis of all provincial, territorial and federal jurisdictions which is most relevant to all Canadians.

  2. Effectiveness - Working assumptions:This iteration to expand coverage of the CCSS allows more consistent and accurate data across all jurisdictions. Given that the CCSS was previously in collection, Statistics Canada has validated the effectiveness of collecting this information directly from institutions to generate statistics on the correctional services population. The current iteration is now expanding the collection to increase the coverage of the dataset, and thus the effectiveness of the insights being derived from it.

    The personal information being collected and linked from existing databases will be used to enhance the analytical capacity to examine the total federal and provincial/territorial correctional populations at a national level once full coverage is achieved. In addition, as more jurisdictions implement the CCSS, more correctional populations across regions can be studied in a more comprehensive manner and be better understood, raising the quality of the analysis of the CCSS as a whole.

    New insights derived from the inclusion of the entire federal and provincial/territorial correctional population in the CCSS will improve traditional indicators to report on disaggregated data, such as producing incarceration rates by Indigenous identity and racialized group. It will also provide more relevant indicators, like re-contact of sentenced individuals after release, to meet the needs of justice stakeholders.

  3. Proportionality: The CCSS collects direct identifiers such as name, address and date of birth of individuals under correctional supervision, as well as demographic characteristics and information relating to their periods of supervision (e.g., their legal hold status, offence and event information). The direct identifiers captured by the CCSS are critical to the proposed record linkage studies. These direct identifiers will be sent to the linkage team to establish linkages with other Statistics Canada datasets.

    Only the variables required to achieve the statistical goals of the survey will be requested in order to mitigate potential impacts to the privacy of the affected individuals under correctional supervision. All data collected by the CCSS are considered the minimum data required to address the data needs and gaps identified by Deputy Ministers, the NJSI, and other partners and stakeholders through the Engagement on Corrections Representation Data and Analysis Strategy.

    Standard best practices with respect to administrative data collection and publication will be followed. Personal identifiers will be removed from the analytical file as soon as operationally feasible and in keeping with Statistics Canada's practices, as outlined in the agency's Generic PIAFootnote 12. The public benefits of the research findings are expected to inform policies and lead to positive changes within correctional services and programs in Canada.

    The CCSS data help fill the need to inform evidence-driven approaches to crime prevention and programs aimed at reducing recidivism, as well as programs designed for rehabilitation, community integration, and public safety. In addition, population-based measures and overrepresentation indicators derived from the CCSS are beneficial to design culturally appropriate programs, address inequities, and engage with communities in a meaningful way. These measures and analyses, as well as the capacity for data disaggregation, are only possible with the use of the personal information collected by the CCSS. The potential benefits and positive changes to social and justice-related programs and services are believed to be proportional to the overall risks to privacy.

  4. Alternatives: Asking for information that has already been captured in administrative data from the jurisdictions and then subsequently through linkage to other administrative data sources would be extremely burdensome and likely of much lower quality, especially in accuracy due to recall errors. Overall, survey collection from individuals is not recommended over administrative data collection and subsequent microdata linkage, as it is the only method to identify the profile of individuals in terms of understanding social, economic, health, and demographic trends related to the correctional population.

    Administrative data from the federal, provincial and territorial correctional services programs in Canada represent the only practical and accurate source of information to collect and meet the national data requirements of the CCSS approved by the National Justice Statistics Initiative in 2014.

    The foundation for the CCSS is an older legacy survey, the Integrated Correctional Services Survey (ICSS), which also collects correctional services microdata for select jurisdictions. However, several socio-demographic variables in the ICSS (e.g., Indigenous status, employment status and educational attainment) do not meet current statistical standards and the personal identifiers collected are insufficient to undertake record linkage with other administrative data sources. Several jurisdictions no longer report to the ICSS and have transitioned to CCSS reporting.

    The intent of the CCSS is to fully replace the ICSS, as well as most components of the other correctional surveys which collect aggregate data only and don't allow data disaggregation or record linkage (i.e., the Adult Correctional Services Survey, the Youth Custody and Community Services Survey and the Corrections Key Indicator Report). As such, administration of the CCSS streamlines data collection and production, reduces respondent burden, improves quality of the data, and increases timeliness of data dissemination.

    The CCSS is the only source of information collected according to standard national requirements that allows disaggregated dataFootnote 13 analysis by categories such as sex, Indigenous identity, and racialized group for the correctional populations in Canada.

Mitigation factors

The overall risk of harm to the survey respondents has been deemed manageable with existing Statistics Canada safeguards that are described in Statistics Canada's Generic Privacy Impact Assessment, with particular emphasis on the following measures:

  • The CCSS uses a separate data processing system for personal identifiers which maintains strict separation between personal identifiers and other data elements collected by the survey. This system has implemented enhanced security measures:
    • (a two-tier system of permissions) for the personal identifier files
    • the data are stored and processed separately
    • the data are accessible to only three employees responsible for processing the data and creating analytical files,
    • the data are never disclosed.
  • Statistics Canada applies strict confidentiality practices and rigorous data quality processes during all production and dissemination activities.
  • Experts at Statistics Canada have been consulted to ensure that the collection of data for the CCSS will be done ethically. The risks for residual disclosure are as low as possible, as access to personal information data is limited to a small number of persons (at any given point in time fewer than 10 persons can view these data).
  • Analytical data files will contain only anonymized identification numbers and will not include any information that would directly identify an individual.
  • For record linkage purposes, at no point during or after the record linkage process are personal identifiers brought together with analytical data in one dataset.
  • CCSS products are vetted by subject matter analysts and methodologists to ensure the identity of persons under correctional supervision is never disclosed directly or indirectly.

Conclusion

This assessment concludes that, with the existing Statistics Canada safeguards, any remaining risks are such that Statistics Canada is prepared to accept and manage the risk.

Missing Persons Data Standards Consultative Engagement

Opened: June 2023

Closed: December 2024

Consultative engagement activities – Phase 3

This consultative engagement initiative deals with topics which may negatively impact the reader due to its subject matter. If you are affected by the issue of missing and murdered Indigenous women, girls and 2SLGBTQQIA+ people and need immediate emotional assistance, please call 1-844-413-6649.

Crown-Indigenous Relations and Northern Affairs Canada has partnered with Statistics Canada to initiate a consultative engagement process in response to Call for Justice 9.5.v, one of the 231 Calls for Justice outlined in the Final Report of the National Inquiry into Missing and Murdered Indigenous Women and Girls: Reclaiming Power and Place The input received as part of this consultative process will form the basis of recommendations leading to the development of national data standards for police services. The objective of this work is to improve the information police gather on missing and murdered Indigenous women and girls, 2SLGBTQQIA+ persons, vulnerable, marginalized, and racialized persons. Reliable and consistent information can play a role in helping to find missing persons and build prevention strategies. Data will allow for regular, continuous, and consistent statistical reporting and monitoring at national, provincial, and sub-provincial levels.

In the first two phases of this initiative, the Canadian Centre for Justice and Community Safety Statistics and Engagement and Data Services Division have heard from several National Indigenous Organizations and other Indigenous organizations and governments, federal, provincial, and territorial government departments, and non-governmental organizations representing marginalized populations.

How to get involved

This consultative engagement initiative is now closed.

Statistics Canada will continue to engage with organizations and governments until the end of 2024 through various formats including virtual group discussions, written submissions, and online forms. Disclosure of personal experiences are not within the scope of this engagement.

If you would like to obtain more information on this engagement initiative or are interested in participating, please contact us by email at consultativeengagement-mobilisationconsultative@statcan.gc.ca.

Statistics Canada is committed to respecting the privacy of participants. All personal information created, held, or collected by the agency is protected by the Privacy Act. For more information on Statistics Canada's privacy policies, please consult the privacy notice.

Results

Summary results of the engagement initiatives will be published online when available.

Variant of NAICS Canada 2022 Version 1.0 – Quarterly Survey of Financial Statements (QSFS)

Industry grouping name Code NAICS codes of the industries included in the grouping
Agriculture, forestry, fishing and hunting 11  
Oil and gas extraction and support services 21A 211110, 211141, 211142, 213111, 213118
Mining and quarrying (except oil and gas) and support activities 21B 212114, 212115, 212116, 212210, 212220, 212231, 212232, 212233, 212291, 212299, 212314, 212315, 212316, 212317, 212323, 212326, 212392, 212393, 212394, 212395, 212396, 212397, 212398 , 213117, 213119
Utilities 22  
Construction 23  
Food and soft drink and ice manufacturing 31A 311111, 311119, 311211, 311214, 311221, 311224, 311225, 311230, 311310, 311340, 311351, 311352, 311410, 311420, 311511, 311515, 311520, 311614, 311615, 311616, 311617, 311619, 311710, 311811, 311814, 311821, 311824, 311830, 311911, 311919, 311920, 311930, 311940, 311990, 312110
Alcohol beverage, tobacco and cannabis product manufacturing 312A 312120, 312130, 312140, 312210, 312220, 312310
Wood product and paper manufacturing 32A 321111, 321112, 321114, 321211, 321212, 321215, 321216, 321217, 321911, 321919, 321920, 321991, 321992, 321999, 322111, 322112, 322121, 322122, 322130, 322211, 322212, 322219, 322220, 322230, 322291, 322299
Petroleum and coal product manufacturing 324  
Basic chemical manufacturing and resin, synthetic rubber, and artificial and synthetic fibres and filaments manufacturing 325A 325110, 325120, 325130, 325181, 325189, 325190, 325210, 325220
Pharmaceutical and medecine manufacturing, and soap, agricultural chemicals, paint and other chemical product manufacturing 325B 325313, 325314, 325320, 325410, 325510, 325520, 325610, 325620, 325910, 325920, 325991, 325999
Plastics and rubber products manufacturing 326  
Non-metallic mineral product manufacturing 327  
Primary metal and fabricated metal product and machinery manufacturing 33A 331110, 331210, 331221, 331222, 331313, 331317, 331410, 331420, 331490, 331511, 331514, 331523, 331529, 332113, 332118, 332210, 332311, 332314, 332319, 332321, 332329, 332410, 332420, 332431, 332439, 332510, 332611, 332619, 332710, 332720, 332810, 332910, 332991, 332999, 333110, 333120, 333130, 333245, 333246, 333247, 333248, 333310, 333413, 333416, 333511, 333519, 333611, 333619, 333910, 333920, 333990
Computer and electronic equipment manufacturing 334  
Motor vehicle and trailer manufacturing 336A 336110, 336120, 336211, 336212, 336215
Motor vehicle parts manufacturing 3363  
Aerospace, rail and ship products and other transportation equipment manufacturing 336B 336410, 336510, 336990, 336611, 336612
Clothing, textile, leather and furniture manufacturing, and other manufacturing 3A 313110, 313210, 313220, 313230, 313240, 313310, 313320, 314110, 314120, 314910, 314990, 315110, 315190, 315210, 315220, 315241, 315249, 315281, 315289, 315990, 316110, 316210, 316990, 323113, 323114, 323115, 323116, 323119, 323120, 335110, 335120, 335210, 335223, 335229, 335311, 335312, 335315, 335910, 335920, 335930, 335990, 337110, 337121, 337123, 337126, 337127, 337213, 337214, 337215, 337910, 337920, 339110, 339910, 339920, 339930, 339940, 339950, 339990
Motor vehicle and motor vehicle parts and accessories merchant wholesalers 415  
Building material and supplies merchant wholesalers 416  
Machinery, equipment and supplies merchant wholesalers 417  
Other wholesalers 41A 411110, 411120, 411130, 411190, 412110, 413110, 413120, 413130, 413140, 413150, 413160, 413190, 413210, 413220, 413310, 413410, 414110, 414120, 414130, 414210, 414220, 414310, 414320, 414330, 414390, 414410, 414420, 414430, 414440, 414450, 414460, 414470, 414510, 414520, 418110, 418120, 418190, 418210, 418220, 418310, 418320, 418390, 418410, 418510, 418610, 418930, 418990, 419110, 419120
Motor vehicle and parts dealers 441  
Food and beverage stores 445  
Clothing, sporting goods, and general merchandise stores 44A 455110, 455211, 455212, 455219, 458111, 458112, 458113, 458114, 458115, 458116, 458119, 458210, 458310, 458320, 459111, 459112, 459113, 459119, 459120, 459130, 459140, 459210
Other retailers 44B 444110, 444120, 444140, 444180, 444230, 444240, 445132, 449110, 449121, 449122, 449123, 449129, 449211, 449212, 449213, 449214, 456110, 456120, 456130, 456191, 456199, 457110, 457120, 457211, 457212, 457219, 459310, 459410, 459420, 459510, 459910, 459920, 459930, 459992, 459993, 459999
Transportation, postal and couriers services, and support activities for transportation 4A 481110, 481214, 481215, 482112, 482113, 482114, 483115, 483116, 483213, 483214, 484110, 484121, 484122, 484210, 484221, 484222, 484223, 484229, 484231, 484232, 484233, 484239, 485110, 485210, 485310, 485320, 485410, 485510, 485990, 487110, 487210, 487990, 488111, 488119, 488190, 488210, 488310, 488320, 488331, 488332, 488339, 488390, 488410, 488490, 488511, 488519, 488990, 491110, 492110, 492210
Pipelines 486  
Warehousing 493  
Publishing, motion picture and sound recording, broadcasting, and information services 51A 512110, 512120, 512130, 512190, 512230, 512240, 512250, 512290, 513110, 513120, 513130, 513140, 513190, 513211, 513212, 516110, 516120, 516211, 516212, 516219, 518210, 519211, 519212, 519290
Telecommunications 517  
Real estate 531  
Rental and leasing of automotive, machinery and equipment, and other goods 53A 532111, 532112, 532120, 532210, 532280, 532310, 532410, 532420, 532490, 533110
Professional, scientific and technical services 54  
Administrative and support, waste management and remediation services 56  
Educational, health care and social assistance services 6A 611110, 611210, 611310, 611410, 611420, 611430, 611510, 611610, 611620, 611630, 611690, 611710, 621110, 621210, 621310, 621320, 621330, 621340, 621390, 621410, 621420, 621494, 621499, 621510, 621610, 621911, 621912, 621990, 622111, 622112, 622210, 622310, 623110, 623210, 623221, 623222, 623310, 623991, 623992, 623993, 623999, 624110, 624120, 624190, 624210, 624220, 624230, 624310, 624410
Arts, entertainment and recreation, and accommodation and food services 7A 711111, 711112, 711120, 711130, 711190, 711213, 711214, 711215, 711217, 711311, 711319, 711321, 711322, 711329, 711411, 711412, 711511, 711512, 711513, 712111, 712115, 712119, 712120, 712130, 712190, 713110, 713120, 713210, 713291, 713299, 713910, 713920, 713930, 713940, 713950, 713991, 713992, 713999, 721111, 721112, 721113, 721114, 721120, 721191, 721192, 721198, 721211, 721212, 721213, 721310, 722310, 722320, 722330, 722410, 722511, 722512
Repair, maintenance and personal services 81A 811112, 811113, 811121, 811122, 811192, 811199, 811210, 811310, 811411, 811412, 811420, 811430, 811490, 812114, 812115, 812116, 812190, 812210, 812220, 812310, 812320, 812330, 812910, 812921, 812922, 812930, 812990
Banking and other depository credit intermediation 5221A 522111, 522112, 522190
Local credit unions 522130  
Credit card issuing, sales financing and consumer lending 5222A 522210, 522220, 522291
All other non-depository credit intermediation 522299  
Central credit unions 522321  
Financial transactions processing, loan brokers, and other activities related to credit intermediation 5223B 522310, 522329, 522390
Securities and commodity contracts dealing 5231A 523110, 523130
Securities and commodity brokerage 5231B 523120, 523140
Miscellaneous Intermediation  523910  
Securities and commodity exchanges, portfolio management and miscellaneous financial investment activity 523A 523210, 523920, 523930, 523990
Life, health and medical insurance carriers 5241A 524111, 524112, 524131, 524132
Property and casualty insurance carriers 5241B 524121, 524122, 524123, 524124, 524125, 524129, 524133, 524134, 524135, 524139
Agencies, brokerages and other insurance related activities 5242  

Quarterly Survey of Financial Statements: Weighted Asset Response Rate - first quarter 2023

Weighted Asset Response Rate
Table summary
This table displays the results of Weighted Asset Response Rate. The information is grouped by Release date (appearing as row headers), 2022, Q1, Q2, Q3, and Q4, and 2023, Q1 calculated using percentage units of measure (appearing as column headers).
Release date 2022 2023
quarterly (percentage)
Q1 Q2 Q3 Q4 Q1
May 24, 2023 81.4 80.9 79.0 72.7 57.6
February 23, 2023 79.3 79.2 76.9 55.2 ..
November 23, 2022 76.2 76.1 56.2 .. ..
August 25, 2022 75.0 55.7 .. .. ..
May 25, 2022 56.7 .. .. .. ..
.. not available for a specific reference period
Source: Quarterly Survey of Financial Statements (2501)

The Rationale Behind Deep Neural Network Decisions

By: Oladayo Ogunnoiki, Statistics Canada

Introduction

In May 2016, Microsoft introduced Tay to the Twittersphere. Tay was an experimental artificial intelligence (AI) chatbot in "conversational understanding". The more you chatted with Tay, the smarter it would become. However, it didn't take long for the experiment to go awry. Tay was supposed to be engaging people in playful conversation, but this playful banter quickly turned into misogynistic and racist commentary.

Of course, the public was perplexed by this turn of events. If this bot was inherently rude, why wouldn't other AI models also go off course? Most Twitter users felt that this bleak event was only a glimmer of what was to come if our future was indeed rich in AI models. However, most data scientists understood the real reason for Tay's negative commentary – the bot was simply repeating what it had learned from the users themselves (Vincent, 2016).

The world of AI continues to grow exponentially and with stories like this happening all the time, there's a strong need to increase the public's trust in AI products. To gain their trust, transparency and explain-ability is of the utmost importance.

One of the primary questions for anyone interacting with an AI model like Tay, is: "why did the model make that decision?" Multiple tools have been designed to explain the rationale behind these models and answer that question. It may be to no one's surprise that visual explanations are an efficient way of explaining this. In their work, Ramprasaath, et al. (2017) outline the requirements of a good visual explanation– they must be class discriminative and should have a high-resolution. These criteria serve as guidelines for identifying the challenge to be addressed: creating a solution that provides a high resolution and class discriminative visual explanation for decisions of a neural network.

Some of the techniques that provide visual explanations include deconvolution, guided backpropagation, class activation mapping (CAM), Gradient-weighted CAM (Grad-CAM), Grad-CAM++, Hi-Res-CAM, Score-CAM, Ablation-CAM, X-Grad-CAM, Eigen-CAM, Full-Grad, and deep feature factorization. For this article, we'll focus on Grad-CAM.

Grad-CAM is an open-source tool that produces visual explanations for decisions from a large class of convolutional neural networks. It works by highlighting the regions of the image that have the highest influence on the final prediction of the deep neural network, thereby providing insight into the decision-making process of the model.

Grad-CAM is based on CAM which uses the activation of the feature maps with respect to the target class. It's specific to certain types of neural networks, such as the Visual Geometry Group network and residual network (ResNet). It uses the gradient of the target class with respect to the feature maps in the final layer. Grad-CAM is a generic method that can be applied to different types of neural networks. Combining features makes Grad-CAM a reliable and accurate tool for understanding the decision-making process of deep neural networks. Guided Grad-CAM is enhanced by incorporating the gradients of the guided backpropagation process to produce a more refined heatmap. One limitation is that it's only able to visualize the regions of the image that are most important for the final prediction, rather than the entire decision-making process of the deep neural network. This means that it may not provide a complete understanding of how the model is making its predictions.

The advantages of Grad-CAM include:

  • No trade off of model complexity and performance for more model transparency.
  • It's applicable to a broad range of convolutional neural networks (CNNs).
  • It's highly class discriminative.
  • Useful for diagnosing failure modes by uncovering biases in datasets.
  • Helps untrained users to recognize a stronger network than a weaker one, even when the predictions are identical.

Methodology

Grad-CAM can be used in multiple computer vision projects such as image classification, semantic segmentation, object detection, image captioning, visual question answering, etc. It can be applied on CNNs and has recently been made available on transformer architectures.

Highlighted below is how Grad-CAM works in image classification, where the objective is to discriminate between different classes:

The process flow of Gradient-weighted class activation mapping (Grad-CAM)
Description - Figure 1The process flow of Gradient-weighted class activation mapping (Grad-CAM)

An image is passed through a CNN and a task specific network to obtain a raw score for the image's class. Next, the gradients are set to zero for all classes except for the desired class, which is set to one. This signal is then backpropagated to the rectified convolutional feature maps of interest, which are combined to compute a blue heatmap that represents where the model needs to look to decide on the class. Finally, the heatmap is pointwise multiplied with guided backpropagation, resulting in guided Grad-CAM visualizations that are high-resolution and concept-specific.

In the case of an image classification task, to obtain the Grad-CAM class-discriminative localization map,LGrad-CAMc
,  for a model on a specific class, the steps below are followed:

  • For a specific class, c, the partial derivative of the score, yc , of the class, c, in respect to feature maps, Ak , of a convolutional layer is calculated using backpropagation.
    ∂yc∂Aijk
  • The gradients flowing back due to backpropagation are pooled via global average pooling. This produces a set of scalars of weights. These are the neuron importance weights.
    αkc= 1Z∑i∑j∂yc∂Aijk
  • The derived scalar weights are applied (linear combination) to the feature map. The result is passed through a Rectified Linear Unit (ReLU) activation function.
    LGrad-CAMc=ReLU∑kαkcAk
  • The result is scaled and applied to the image, highlighting the focus of the neural network. As seen, a ReLU activation function is applied to the linear combination of maps, because it's only interested in the pixels or features that have a positive influence on the class score, yc .

Demonstration of Grad-CAM

A pair of cats and a pair of remote controls
Description - Figure 2A pair of cats and a pair of remote controls

Image consisting of two Egyptian cats lying down on a pink sofa with remote controls on the left-hand side of each cat.

Figure 2 is an image of two Egyptian cats and two remote controls. The image was derived from the Hugging Face's cat image dataset, using their Python library. The objective is to identify the items within the image using different pretrained deep learning models. A PyTorch package called the PyTorch-GradCAM is used. The Grad-CAM feature identifies aspects of the image that activate the feature map of the Egyptian cat class and the remote-control class. After following the PyTorch-GradCAM tutorial, the Grad-CAM results are replicated for different deep neural networks.

Grad-CAM results of a pretrained Resnet-50 architecture to classify the figure 2 image. This image was generated by applying Grad-CAM to figure 2 in a Jupyter Notebook.
Description - Figure 3Grad-CAM results of a pretrained Resnet-50 architecture to classify the figure 2 image. This image was generated by applying Grad-CAM to figure 2 in a Jupyter Notebook.

Heatmap images generated from a Resnet-50 architecture using Grad-CAM for the Egyptian cat class (left) and Remote-control class (right). The intensity of the red colour shows the regions that contribute the most to the model decision. There are few intense regions for the cat, while the remotes are almost fully captured, but not highly intense.

Figure 2 is parsed through a pretrained residual neural network (Resnet-50) as per the PyTorch-Grad-CAM tutorial. Figure 3 is the image generated using Grad-CAM. For the Egyptian cat class, the leg, stripes, and faces of the cats activated the feature map. For the remote controls, the buttons and profile are what activated the feature map. The top 5k predicted classes in order of logit, are remote control, tiger cat, Egyptian cat, tabby cat, and pillow. This model seems to be more confident the image contains remote controls and cats. Though less confident, the pillow category made the top five of the listed categories. This could be because the model was trained with cat-printed pillows.

Grad-CAM results of a pretrained shifted window transformer to classify figure 2. This image was generated by applying Grad-CAM to figure 2 in a Jupyter Notebook.
Description - Figure 4Grad-CAM results of a pretrained shifted window transformer to classify figure 2. This image was generated by applying Grad-CAM to figure 2 in a Jupyter Notebook.

Heatmap images generated from a shifted window transformer using Grad-CAM for the Egyptian cat class (left) and remote-control class (right). The intensity of the red colour shows the regions that contribute the most to the model's decision. The cats show more intense regions, while the remote controls are almost fully captured with high-intensity.

Like the Resnet-50 architecture, the same image is parsed through a pretrained shifted window transformer. Figure 4 shows the cats' fur, stripes, faces, and legs as activated regions in the feature map in respect to the Egyptian cat category. The same occurs in relation to the feature map in respect to the remote controls. The top 5k predicted classes, in order of logit, are tabby cat, tiger cat, domestic cat, and Egyptian cat. This model is more confident that cats are in this image than remote controls.

Grad-CAM results of a pretrained vision transformer architecture in classifying the image in figure 2 This image was generated by applying Grad-CAM to figure 2 in a Jupyter notebook.
Description - Figure 5Grad-CAM results of a pretrained vision transformer architecture in classifying the image in figure 2 This image was generated by applying Grad-CAM to figure 2 in a Jupyter notebook.

Heatmap images generated from a Vision transformer using Grad-CAM for the Egyptian cat class (left) and remote-control class (right). The intensity of the red colour shows the regions that contribute the most to the model decision. The cats are fully captured in high intensity. The remotes are also captured but not equivalent intensity. In addition, other regions of the images are highlighted despite not being part of either class.

As seen above, more regions of the feature map are activated, including sections of the image that didn't include cat features. The same occurs for regions of the feature map in respect to the remote-control class. The top 5k predicted classes, in order of logit, are Egyptian cat, tiger cat, tabby cat, remote control, and lynx.

The Grad-CAM results with the top 5k categories for different architectures can be used to favour a selection of the vision transformer (VIT) architecture for tasks related to identifying Egyptian cats and remote controls.

Conclusion

Some of the challenges in the field of AI includes increasing the trust of people in the developed models and understanding the rationale behind the decision making of these models during development. Visualizations tools like Grad-CAM provide insight into these rationales and aid in highlighting different failure modes of AI models for specific tasks. It can be used to identify errors in the models and improve their performance. On top of Grad-CAM, there are other visualization tools that have been developed such as Score-CAM, which performs even better in interpreting the decision-making process of deep neural networks. Though Grad-CAM will be selected over Score-CAM due it's simplicity and agnosticism to model architectures. The use of tools such as Grad-CAM, should be encouraged in visually explaining the reason behind the decisions of AI models.

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References

  • S. R. Ramprasaath, C. Michael, D. Abhishek, V. Ramakrishna, P. Devi and B. Dhruv, "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization," in ICCV, IEEE Computer Society, 2017, pp. 618-626.
  • Z. Bolei, K. Aditya, L. Agata, O. Aude and T. Antonio, "Learning Deep Features for Discriminative Localization," CoRR, 2015.
  • J. Vincent, "Twitter taught Microsoft's AI chatbot to be racist in less than a day", in The Verge, 2016.
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National Travel Survey: C.V.s for Person-Trips by Duration of Trip, Main Trip Purpose and Country or Region of Trip Destination – Q4 2022

National Travel Survey: C.V.s for Person-Trips by Duration of Trip, Main Trip Purpose and Country or Region of Trip Destination – Q4 2022
Table summary
This table displays the results of C.V.s for Person-Trips by Duration of Trip, Main Trip Purpose and Country or Region of Trip Destination. The information is grouped by Duration of trip (appearing as row headers), Main Trip Purpose, Country or Region of Trip Destination (Total, Canada, United States, Overseas) calculated using Person-Trips in Thousands (× 1,000) and C.V. as a units of measure (appearing as column headers).
Duration of Trip Main Trip Purpose Country or Region of Trip Destination
Total Canada United States Overseas
Person-Trips (x 1,000) C.V. Person-Trips (x 1,000) C.V. Person-Trips (x 1,000) C.V. Person-Trips (x 1,000) C.V.
Total Duration Total Main Trip Purpose 67,564 A 60,934 A 5,217 A 1,413 A
Holiday, leisure or recreation 21,112 A 17,744 A 2,494 B 874 A
Visit friends or relatives 28,448 A 26,908 A 1,137 B 403 B
Personal conference, convention or trade show 1,174 C 1,047 C 124 D 3 E
Shopping, non-routine 4,872 B 4,140 B 731 B 2 E
Other personal reasons 5,519 B 5,326 B 157 C 36 D
Business conference, convention or trade show 1,711 B 1,363 B 295 C 53 C
Other business 4,727 B 4,405 B 278 C 44 C
Same-Day Total Main Trip Purpose 43,435 A 41,626 A 1,809 B ..  
Holiday, leisure or recreation 11,991 A 11,400 B 591 C ..  
Visit friends or relatives 17,946 A 17,632 A 314 C ..  
Personal conference, convention or trade show 817 C 781 C 36 E ..  
Shopping, non-routine 4,512 B 3,869 B 643 B ..  
Other personal reasons 4,326 B 4,264 B 62 D ..  
Business conference, convention or trade show 456 C 436 C 21 E ..  
Other business 3,387 B 3,244 B 143 E ..  
Overnight Total Main Trip Purpose 24,129 A 19,308 A 3,408 A 1,413 A
Holiday, leisure or recreation 9,121 A 6,344 A 1,904 A 874 A
Visit friends or relatives 10,502 A 9,276 A 823 B 403 B
Personal conference, convention or trade show 357 C 266 C 88 D 3 E
Shopping, non-routine 360 C 271 C 88 C 2 E
Other personal reasons 1,193 B 1,062 B 95 C 36 D
Business conference, convention or trade show 1,255 B 928 B 275 C 53 C
Other business 1,340 B 1,161 B 135 C 44 C
..
data not available

Estimates contained in this table have been assigned a letter to indicate their coefficient of variation (c.v.) (expressed as a percentage). The letter grades represent the following coefficients of variation:

A
c.v. between or equal to 0.00% and 5.00% and means Excellent
B
c.v. between or equal to 5.01% and 15.00% and means Very good.
C
c.v. between or equal to 15.01% and 25.00% and means Good.
D
c.v. between or equal to 25.01% and 35.00% and means Acceptable.
E
c.v. greater than 35.00% and means Use with caution.

Monthly Survey of Food Services and Drinking Places: CVs for Total Sales by Geography – March 2023

Monthly Survey of Food Services and Drinking Places: CVs for Total Sales by Geography - March 2023
Table summary
This table displays the results of CVs for Total sales by Geography. The information is grouped by Geography (appearing as row headers). Month and percentage (appearing as column headers).
Geography Month
202203 202204 202205 202206 202207 202208 202209 202210 202211 202212 202301 202302 202303
percentage
Canada 0.87 0.45 0.51 0.66 0.49 0.14 0.13 0.17 0.24 0.88 0.32 0.40 0.29
Newfoundland and Labrador 1.20 1.52 1.66 0.53 0.50 0.47 0.49 0.73 0.49 0.93 2.43 0.89 1.19
Prince Edward Island 9.73 15.01 6.85 15.97 9.23 5.27 3.04 8.45 8.22 3.45 10.49 14.28 2.20
Nova Scotia 0.50 0.98 1.16 1.79 3.37 0.43 0.40 0.37 0.43 16.87 0.83 0.97 0.84
New Brunswick 0.55 1.41 1.26 0.67 0.53 0.52 0.50 0.56 0.73 12.18 1.21 1.95 1.18
Quebec 1.95 0.53 1.73 1.55 0.97 0.18 0.28 0.26 0.19 1.73 0.67 0.96 0.83
Ontario 1.19 0.80 0.74 1.30 0.95 0.25 0.25 0.21 0.53 0.73 0.67 0.85 0.51
Manitoba 0.54 0.80 0.97 0.68 3.49 0.48 0.40 0.37 0.58 9.72 0.78 0.91 1.48
Saskatchewan 1.18 1.84 5.77 6.45 4.85 1.30 0.73 1.31 1.44 7.51 0.62 1.47 1.28
Alberta 2.01 0.68 0.57 1.45 0.91 0.39 0.30 0.33 0.38 1.56 0.40 0.49 0.47
British Columbia 3.25 1.55 0.97 0.64 0.91 0.28 0.21 0.66 0.33 2.77 0.44 0.47 0.50
Yukon Territory 2.20 2.07 23.00 3.32 2.54 2.09 2.07 2.34 2.20 2.50 41.12 3.45 33.49
Northwest Territories 1.77 3.19 29.08 3.20 2.74 2.38 2.05 2.00 2.09 2.56 6.03 2.73 40.91
Nunavut 0.76 0.69 73.56 1.55 1.52 1.30 2.35 2.85 101.77 43.21 2.83 2.40 117.22

Wholesale Trade Survey (monthly): CVs for total sales by geography - March 2023

Wholesale Trade Survey (monthly): CVs for total sales by geography - March 2023
Geography Month
202203 202204 202205 202206 202207 202208 202209 202210 202211 202212 202301 202302 202303
percentage
Canada 0.6 0.8 0.8 0.6 0.7 0.6 0.6 0.6 0.6 0.7 0.7 0.6 0.5
Newfoundland and Labrador 1.5 1.9 0.5 0.3 0.3 0.6 0.5 0.5 0.6 0.5 0.6 0.3 0.3
Prince Edward Island 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nova Scotia 2.5 2.7 3.5 1.6 4.7 2.5 1.9 2.9 1.8 4.9 4.4 2.0 3.8
New Brunswick 1.4 2.9 1.3 1.2 2.1 3.0 1.7 1.3 2.6 2.4 1.8 1.9 1.4
Quebec 1.4 2.5 1.9 1.4 1.5 1.4 1.7 1.4 1.5 2.1 1.6 1.4 1.4
Ontario 1.1 1.2 1.3 1.1 1.1 0.9 1.0 0.9 0.9 1.1 1.1 1.0 1.1
Manitoba 0.6 0.8 1.8 1.7 1.2 1.0 1.5 2.1 1.4 1.8 0.8 0.7 0.5
Saskatchewan 0.4 0.6 0.7 0.7 0.6 1.1 1.2 0.5 0.7 0.4 0.4 0.4 0.6
Alberta 0.8 1.8 1.2 1.2 1.4 1.4 0.8 1.4 1.3 1.1 1.4 0.9 0.4
British Columbia 1.6 1.4 1.6 2.1 1.9 1.6 1.8 2.6 1.5 1.4 1.5 1.8 1.7
Yukon Territory 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Northwest Territories 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Nunavut 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

National Travel Survey: C.V.s for Visit-Expenditures by Duration of Visit, Main Trip Purpose and Country or Region of Expenditures – Q4 2022

National Travel Survey: C.V.s for Visit-Expenditures by Duration of Visit, Main Trip Purpose and Country or Region of Expenditures, including expenditures at origin and those for air commercial transportation in Canada, in Thousands of Dollars (x 1,000)
Table summary
This table displays the results of C.V.s for Visit-Expenditures by Duration of Visit, Main Trip Purpose and Country or Region of Expenditures. The information is grouped by Duration of trip (appearing as row headers), Main Trip Purpose, Country or Region of Expenditures (Total, Canada, United States, Overseas) calculated using Visit-Expenditures in Thousands of Dollars (x 1,000) and c.v. as units of measure (appearing as column headers).
Duration of Visit Main Trip Purpose Country or Region of Expenditures
Total Canada United States Overseas
$ '000 C.V. $ '000 C.V. $ '000 C.V. $ '000 C.V.
Total Duration Total Main Trip Purpose 23,128,455 A 14,493,645 A 5,755,989 A 2,878,821 A
Holiday, leisure or recreation 10,772,424 A 5,007,498 A 3,846,923 B 1,918,003 B
Visit friends or relatives 5,909,523 A 4,567,851 A 727,233 B 614,438 B
Personal conference, convention or trade show 351,279 B 242,466 B 104,174 D 4,638 E
Shopping, non-routine 1,104,183 B 903,613 B 198,218 C 2,352 E
Other personal reasons 1,262,310 B 1,051,208 B 100,216 D 110,887 D
Business conference, convention or trade show 1,685,083 B 1,047,344 B 520,784 C 116,955 C
Other business 2,043,654 B 1,673,666 B 258,441 C 111,548 C
Same-Day Total Main Trip Purpose 5,339,135 A 4,974,751 A 352,707 B 11,677 E
Holiday, leisure or recreation 1,710,365 B 1,548,456 B 150,746 C 11,163 E
Visit friends or relatives 1,507,953 B 1,439,415 B 68,538 E ..  
Personal conference, convention or trade show 96,966 C 89,167 C 7,284 E 515 E
Shopping, non-routine 866,286 B 756,192 B 110,094 C ..  
Other personal reasons 604,099 B 599,079 B 5,020 E ..  
Business conference, convention or trade show 73,678 D 67,843 E 5,836 E ..  
Other business 479,788 C 474,599 C 5,189 E ..  
Overnight Total Main Trip Purpose 17,789,320 A 9,518,894 A 5,403,282 A 2,867,144 A
Holiday, leisure or recreation 9,062,058 A 3,459,042 B 3,696,176 B 1,906,840 B
Visit friends or relatives 4,401,570 A 3,128,436 A 658,695 B 614,438 B
Personal conference, convention or trade show 254,313 C 153,299 C 96,890 D 4,123 E
Shopping, non-routine 237,897 C 147,421 C 88,124 D 2,352 E
Other personal reasons 658,211 B 452,129 B 95,196 D 110,887 D
Business conference, convention or trade show 1,611,405 B 979,501 B 514,948 C 116,955 C
Other business 1,563,866 B 1,199,067 B 253,252 D 111,548 C
..
data not available

Estimates contained in this table have been assigned a letter to indicate their coefficient of variation (c.v.) (expressed as a percentage). The letter grades represent the following coefficients of variation:

A
c.v. between or equal to 0.00% and 5.00% and means Excellent.
B
c.v. between or equal to 5.01% and 15.00% and means Very good.
C
c.v. between or equal to 15.01% and 25.00% and means Good.
D
c.v. between or equal to 25.01% and 35.00% and means Acceptable.
E
c.v. greater than 35.00% and means Use with caution.

National Travel Survey Q4 2022: Response Rates

National Travel Survey: Response Rate – Q4 2022
Table summary
This table displays the results of Response Rate. The information is grouped by Province of residence (appearing as row headers), Unweighted and Weighted (appearing as column headers), calculated using percentage unit of measure (appearing as column headers).
Province of residence Unweighted Weighted
Percentage
Newfoundland and Labrador 20.3 17.7
Prince Edward Island 15.2 14.3
Nova Scotia 24.6 21.9
New Brunswick 23.0 19.7
Quebec 28.4 25.0
Ontario 26.1 24.0
Manitoba 27.1 23.8
Saskatchewan 26.6 23.1
Alberta 24.8 23.1
British Columbia 26.7 24.9
Canada 25.6 24.1
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